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this paper presents a new approach to extract image features for texture classification. The extracted features are obtained by a dominant-completed modeling of the traditional local binary pattern (LBP) operator, which is robust to image rotation, grey scale changing and insensitive to noise and histogram equalization. The main idea of this texture classification approach is that a dominant center...
Terrain classification in field environment for mobile robot is affected by weather conditions among which illumination diversification plays a major role. With the changes in feature extraction method, classification result will vary significantly in the changing environment. This paper introduces a method of illumination recognition by analyzing the illumination distribution in visual space and...
Some tactual communication tools which conveys emotions during face-to-face oral conversation have been developing. In the present study, to diagnose the mental condition of the institutionalized elderly person using the tactual communication tool, an algorithm of emotion recognition was developed. The emotion recognition algorithm recognized six emotions (surprise, joy, anger, sadness, disgust and...
Histograms of Oriented Gradients (HOG) feature has been successfully used in pedestrian detection and achieves high accuracy. This paper introduces a content retrieval algorithm based on improved HOG. The method has two steps which are adjusting the HOG structure by scanning the image with a sliding HOG window and reducing feature dimension by principle component analysis (PCA) technique. The experimental...
Panchromatic (PAN) satellite imagery comprises only a single band but it has finer resolution in comparison to the multi-spectral band imagery. In the case of feature extraction and classification, although the multi-spectral imagery has an advantage in availability of the different aspect of spectral properties of the ground coverage, the recent studies introducing intelligent classification and...
In the past SAR data has been proven as a great source for land cover characterization. For classification purpose many individual methods has been used, but single method are likely to undergo high variance or biasness depending on the base used for classification. Hence, in this paper random forest classification technique has been used for SAR data classification into different land cover classes...
Kernel-based image classification methods rely on the considered kernel functions that can be chosen with respect to prior information on the adopted features. In remote sensing, histogram features have recently gained an increasing interest due to their capability to address several critical classification problems (e.g., the problem of curse of dimensionality) when appropriate kernels and classifiers...
In this paper, we propose a theoretically new and effective feature for SAR image classification. The new feature combines traditional gray level co-occurrence matrix (GLCM) textural feature and the recent multilevel local pattern histogram (MLPH) feature. It can not only describe intrinsic property of land-cover/land-use surfaces, corresponding to textural information, but it also captures both local...
The tracking of moving biological cells in time-lapse video sequences is fundamental to further understanding biological processes. Automatic cell tracking techniques require accurate cell image segmentation; however, current segmentation techniques are susceptible to errors due to non-ideal but realistic cell image conditions, including low contrast typical of cell microscopic images. This paper...
In this paper, we propose a new system for isolated sign language recognition (SLR) and continuous SLR. In isolated SLR, Histogram of Oriented Displacement is used to describe the trajectories, and multi-SVM is adopted for classification. In continuous SLR, we propose a Dynamic Programming method with warping templates obtained by Dynamic Time Warping (DTW) algorithm. We evaluate our approach with...
In this study, we apply an anomaly-based approach to analyze traffic flows transferred over a network to detect the flows related to different types of attacks. Based on the information extracted from network flows a model of normal user behavior is discovered with the help of several clustering techniques. This model is then used to detect anomalies within recent time intervals. Since this approach...
The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective...
Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation...
In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed...
This paper proposes the innovative features of tempogram for the selection of predominant tempo in a two-stage tempo estimation system. At stage one, a tempo-pair estimator identifies the dominant tempo pair from a given audio music. At stage two, the statistical features called tempogram shape vector (tsv) discriminates the predominant tempo from the identified tempo pair. Our experiments demonstrate...
For music identification, conventional bag of audio words model methods generally compute a histogram for a piece of music, which ignores the temporal characteristic of music and has a negative influence on the accuracy. In addition, they are usually based on DFT spectrogram, which cannot represent music as well as Constant Q (CQ) spectrogram. To address the above problems, we propose a two-layer...
A key development in the design of visual object recognition systems is the combination of multiple features. In recent years, various popular optimization based feature combination methods have been proposed in the literatures. However, those methods obtain tiny performance improvement at the cost of enormous computation consumption. In this paper, we propose an improved averaging combination (IAC)...
Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of...
Digital Out Of Home (DOOH) applications which exploit computer vision algorithms to automatically collect soft biometrics of people in front a smart screen are of great interest for industry. In the last years many gender recognition pipelines have been proposed in literature. Different benchmark datasets have been introduced and used for testing purpose. This paper gives an overview of the state-of-the-art...
Mobile image retrieval and pairwise matching applications pose a unique set of challenges. As communicating large amount of data could take tens of seconds over a slow wireless link, MPEG defined the CDVS standard to transfer over the network only the data essential to the matching, and not the entire image. However, the extraction of salient image features is a very time consuming process, and it...
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